Assignment 8: Visualization - Making a bar race

How to do it?:

• Open the Rmarkdown file of this assignment (link) in Rstudio.

• Right under each question, insert a code chunk (you can use the hotkey Ctrl + Alt + I to add a code chunk) and code the solution for the question.

• Knit the rmarkdown file (hotkey: Ctrl + Alt + K) to export an html.

• Publish the html file to your Githiub Page.

Submission: Submit the link on Github of the assignment to Canvas

---

library(tidyverse)
library(gganimate)
library(gifski)
df <- read.csv("adult_census.csv")

1. Install two packages gganimate and gifski then restart Rstudio. Using the Adult Census Income data, make an animation using geom_point and transition_states.

df %>% ggplot(aes(x = hours.per.week,
           y = age, color=race))+
  geom_point()+
  transition_states(sex)+
  labs(title = 'sex: {closest_state}')

2. Using the Adult Census Income data, make an animation using geom_bar and transition_states.

df %>% ggplot(aes(x = hours.per.week,
           y = age, color=marital.status))+
  geom_point()+
  transition_states(sex)+
  labs(title = 'sex: {closest_state}')

3. Use the WHO’s dataset at this link. Make a top-10 bar race by months between countries on the number of deaths by Covid 19 in 2021.

library(gapminder)
library(gganimate)
library(ggplot2)
library(tidyverse)
library(lubridate)
library(knitr)
df <- read_csv("WHO-COVID-19-global-data.csv")

df$week <- week(df$Date_reported)
d1 <- df %>% group_by(week, Country) %>% summarise(mean = mean(New_cases))
d2 <- d1 %>% group_by(week) %>% mutate(rank=rank(-mean)) 
d3 <- d2 %>% filter(rank <= 10)
a1 <- d3 %>% ggplot(aes(x=rank, y=mean, group=Country, fill=Country, label=Country)) + geom_col()+geom_text(aes(y = mean, label = Country), hjust = 1.4)+ 
    coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
    labs(title = 'Week {closest_state}', x='', y='Total Number of Positive Caeses', fill='Country')+
    theme(plot.title = element_text(hjust = 1, size = 22),
          axis.ticks.y = element_blank(),
          axis.text.y  = element_blank()) + 
    transition_states(week)+
    ease_aes("cubic-in-out")
animate(a1, nframes = 400)

4. Make a bar race using a dataset of your own interest. You may use the dataset that we use in class (https://covidtracking.com/data/download/all-states-history.csv) but you should make a different bar racev from ones in the slides.

df <- read.csv("all-states-history.csv")

df$week <- week(df$date)
d1 <- df %>% group_by(week, state) %>% summarise(mean = mean(hospitalizedCurrently))
d2 <- d1 %>% group_by(week) %>% mutate(rank=rank(-mean)) 
d3 <- d2 %>% filter(rank <= 10)
a1 <- d3 %>% ggplot(aes(x=rank, y=mean, group=state, fill=state, label=state)) + geom_col()+
    geom_text(aes(y = mean, label = state), hjust = 1.4)+ 
    coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
    labs(title = 'Week {closest_state}', x='', y='Total Number of Positive Caeses', fill='state')+
    theme(plot.title = element_text(hjust = 1, size = 22),
          axis.ticks.y = element_blank(),
          axis.text.y  = element_blank()) + 
    transition_states(week)+
    ease_aes("cubic-in-out")
animate(a1, nframes = 400)

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